38,212 research outputs found
Interactive Constrained Association Rule Mining
We investigate ways to support interactive mining sessions, in the setting of
association rule mining. In such sessions, users specify conditions (queries)
on the associations to be generated. Our approach is a combination of the
integration of querying conditions inside the mining phase, and the incremental
querying of already generated associations. We present several concrete
algorithms and compare their performance.Comment: A preliminary report on this work was presented at the Second
International Conference on Knowledge Discovery and Data Mining (DaWaK 2000
Matrix completion with queries
In many applications, e.g., recommender systems and traffic monitoring, the
data comes in the form of a matrix that is only partially observed and low
rank. A fundamental data-analysis task for these datasets is matrix completion,
where the goal is to accurately infer the entries missing from the matrix. Even
when the data satisfies the low-rank assumption, classical matrix-completion
methods may output completions with significant error -- in that the
reconstructed matrix differs significantly from the true underlying matrix.
Often, this is due to the fact that the information contained in the observed
entries is insufficient. In this work, we address this problem by proposing an
active version of matrix completion, where queries can be made to the true
underlying matrix. Subsequently, we design Order&Extend, which is the first
algorithm to unify a matrix-completion approach and a querying strategy into a
single algorithm. Order&Extend is able identify and alleviate insufficient
information by judiciously querying a small number of additional entries. In an
extensive experimental evaluation on real-world datasets, we demonstrate that
our algorithm is efficient and is able to accurately reconstruct the true
matrix while asking only a small number of queries.Comment: Proceedings of the 21th ACM SIGKDD International Conference on
Knowledge Discovery and Data Minin
On the Semantics of "Now" in Databases
While "now" is expressed in SQL as CURRENT-TIMESTAMP within queries, this value cannot be
stored in the database. However, this notion of an ever-increasing current-time value has been
reflected in some temporal data models by inclusion of database-resident variables, such as
"now," "until-changed," "â," "@" and "-." Time variables are very desirable, but their use
also leads to a new type of database, consisting of tuples with variables, termed a variable
database.
This paper proposes a framework for defining the semantics of the variable databases of temporal
relational data models. A framework is presented because several reasonable meanings
may be given to databases that use some of the specific temporal variables that have appeared
in the literature. Using the framework, the paper defines a useful semantics for such databases.
Because situations occur where the existing time variables are inadequate, two new types of
modeling entities that address these shortcomings, timestamps which we call now-relative and
now-relative indeterminate, are introduced and defined within the framework. Moreover, the paper
provides a foundation, using algebraic bind operators, for the querying of variable databases
via existing query languages. This transition to variable databases presented here requires minimal
change to the query processor. Finally, to underline the practical feasibility of variable
databases, we show that database variables can be precisely specified and efficiently implemented
in conventional query languages, such as SQL, and in temporal query languages, such
as TSQL2.Information Systems Working Papers Serie
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